• Proceedings of the Korean Institute of Building Construction Conference
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• v.y2004m10
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• pp.67-70
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• 2004

The ubiquitous computing environment is widely expanded in almost all industries such as transportation. physical distribution, manufacturing, communication, tourist industry, financial business, and so on. But the construction industry is not well applied yet. Thus this study investigate the ubiquitous computing environment and propose a way for applying this technology to the construction industry. To achieve this aim, this study consider about a instrumentation management of retaining wall system in earthmoving operations, and suggest the ubiquitous system configuration to measure the variance of retaining wall. Through this system, the efficiency of the instrumentation management of retaining wall will be improved.

• Management Science and Financial Engineering
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• v.12 no.2
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• pp.19-35
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• 2006

We use the hierarchical Bayesian approach to describe the transition probabilities of a binary nonhomogeneous Markov chain. The Markov chain is used for describing the transition behavior of emotionally disturbed children in a treatment program. The effects of covariates on transition probabilities are assessed using a logit link function. To describe the time evolution of transition probabilities, we consider two modeling strategies. The first strategy is based on the concept of exchangeabiligy, whereas the second one is based on a first order Markov property. The deviance information criterion (DIC) measure is used to compare models with two different time dependent structures. The inferences are made using the Markov chain Monte Carlo technique. The developed methodology is applied to some real data.

• Korean Management Science Review
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• v.32 no.2
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• pp.15-35
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• 2015

Large business groups typically have central firms through which their controlling families establish (or acquire) new firms and maintain control over other member firms. Research on corporate governance has developed metrics to identify those central firms and investigated an impact of the centrality on ownership structure and firm's financial performance. This paper introduces centrality metrics used in social network analysis (SNA) to measure how crucial a role each firm plays in the ownership structure of its business group. Then, the SNA centrality metrics are compared with the metrics developed in corporate governance field. Also, we test the relationship between the SNA centrality metrics and firm's value. Experimental results show that the SNA centrality metrics are closely correlated with the centrality metrics used in corporate governance and are significantly correlated with firm's value.

• Asia pacific journal of information systems
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• v.11 no.3
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• pp.105-126
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• 2001

The objective of research is to find out exogenous variables that influence the usage and performance EDI in the Korea firms. Specifically the goals of this research are; (1) to examine relationships between exogenous variables, such as management, technology, inter-firm relationship characteristics, and EDI performance, and (2) to measure EDI volume, EDI diversity, EDI depth among Korea firms. The questionnaire consists of two versions; One for the EDI system managers and the other for the EDI users. The analysis of this study is designed as cross level to examine the causal relationship among variables in different analysis level. The reliability and validity of data was tested by explanatory factor analysis, Cronbach's alpha coefficient, confirmatory factor analysis, and correlation analysis. Also, the structural equation model(SEM) analysis was performed to test the usefulness of the model. The analysis results revealed that education level, IS growth, trust, support, power are major influential variables on the usage level and performance of EDI. Especially, persuasive power turned out to be more important than coercive power, and technical financial support from organizations was also found to be a significant variables.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.421-433
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• 2017

The CTE (conditional tail expectation) is a useful risk management measure for a diversified investment portfolio that can be generally estimated by using a transformed univariate distribution. Hong et al. (2016) proposed a multivariate CTE based on multivariate quantile vectors, and explored its characteristics for multivariate normal distributions. Since most real financial data is not distributed symmetrically, it is problematic to apply the CTE to normal distributions. In order to obtain a multivariate CTE for various kinds of joint distributions, distribution fitting methods using copula functions are proposed in this work. Among the many copula functions, the Clayton, Frank, and Gumbel functions are considered, and the multivariate CTEs are obtained by using their generator functions and parameters. These CTEs are compared with CTEs obtained using other distribution functions. The characteristics of the multivariate CTEs are discussed, as are the properties of the distribution functions and their corresponding accuracy. Finally, conclusions are derived and presented with illustrative examples.

• Management & Information Systems Review
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• v.25
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• pp.43-68
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• 2008

From intuitional viewpoint many researchers have been considering that information technology investment serves to increase the productivity and the profitability of firm. But the empirical studies that have examined the relationship between information technology investment and firm performance have reported mixed findings. In spite of that, recently there has been growing recognition of the importance of assessing information technology investment in determining future performance of firms. This study examines the relationship between investment in information technology and performance of securities companies in Korea. I use Tobin's Q, a financial market-based measure of firm performance and investigates the pure effect of information technology investment on firm performance after controlling for a variety of firm specific variables which may affect on firm performance. This study finds that information technology investment have a significantly positive association with Tobin's Q. This result implicates that information technology investment contributes to a firm's future performance potential.

• KDI Journal of Economic Policy
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• v.42 no.3
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• pp.33-52
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• 2020

Patents are widely used in the literature as a measure of firm-level innovation. It is regarded that patents improve a firm's operational environment and ultimately increase the value of the firm. However, the relationship between patents and firm value in Korea is under-explored in the literature due to the difficulty of constructing datasets. This paper examines whether patents in Korea increase the market value of a firm. To do this, I exploit novel data on firm-level patents and financial information of all listed Korean companies during the period of 1993-2015 and estimate the non-linear production-function type of Tobin's q equations on R&D, patents, and citations. Surprisingly, I find that patents and citations are weakly associated with firm value, while R&D is strongly associated with an increase in firm value. These results direct imply that policymakers in Korea should enhance patenting incentives to encourage firms to innovate.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.4
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• pp.321-333
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• 2003

Although manufacturing cost is a major part of profit in a company, it is difficult to be calculated by an analytic method. Besides, the manufacturing cost gained by simple financial structure dose not have an important meaning in market place. Therefore, an analytic method of computing the manufacturing cost is very necessary in manufacturing system. In this study, we suggested the direct cost analysis model which are able to measure accurate cost analysis of product in manufacturing system. The direct cost analysis model is made up of directly used expenditure for unit product. Also, system performances are put in the manufacturing cost analysis model so that it could be possible to analyze the change of manufacturing cost as system performances change. At the end of this paper, it verifies its relevancy and practicality of the suggested direct cost analysis model through the case study, using real data for direct labor cost.

• Journal of Biosystems Engineering
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• v.27 no.2
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• pp.107-116
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• 2002

This study was designed to analyze farmers' behavior on dealing with secondhand agricultural machinery and to device proper measures for promoting utilization of secondhand agricultural machinery. This objective was accomplished by using data which were analyzed, collected through questionnaires from 35 farmers on dealing with secondhand agricultural machinery. The main, realistic measures for promoting utilization of secondhand agricultural machinery, based on farmers' behavior, are as fellows: First, to make and offer a criteria fer evaluating the optimal price of secondhand agricultural machinery, which should be a effective measure for promoting farmers' dealing with them. Second. to establish a store to exhibit and sell secondhand agricultural machinery. This is the most important way to promote dealing with them. In the process of political supporting to establish the store, the equality of opportunity should be adopted to every organization want to deal secondhand agricultural machinery. Third, to strengthen financial supports for buying secondhand agricultural machinery and to assure A/S system.

• Communications for Statistical Applications and Methods
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• v.22 no.4
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• pp.321-331
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• 2015

The K-means clustering algorithm is a popular and widely used method for clustering. For covariance matrices, we consider a geodesic clustering algorithm based on the K-means clustering framework in consideration of symmetric positive definite matrices as a Riemannian (non-Euclidean) manifold. This paper considers a geodesic clustering algorithm for data consisting of symmetric positive definite (SPD) matrices, utilizing the Riemannian geometric structure for SPD matrices and the idea of a K-means clustering algorithm. A K-means clustering algorithm is divided into two main steps for which we need a dissimilarity measure between two matrix data points and a way of computing centroids for observations in clusters. In order to use the Riemannian structure, we adopt the geodesic distance and the intrinsic mean for symmetric positive definite matrices. We demonstrate our proposed method through simulations as well as application to real financial data.